Hello, and welcome to the Physics World Weekly
podcast,
which is sponsored by LiveInno,
the Liverpool Centre For Doctoral Training For Innovation
in Data Intensive Science.
The UK based CDT
is a partnership
between the University of Liverpool and Liverpool John
Moores University.
Since 2022,
LiveInno
has offered 3 cohorts of students,
4 year, fully funded PhD
studentships
in data intensive science.
From medical physics to quantum computing,
the CDT has a diverse portfolio
of research projects,
but students all share training in high performance
computing,
machine learning, and AI,
and data analysis.
The CDT
also gives students access to career advice and
training in project management,
entrepreneurship,
and communication
skills.
As well as working on their original research,
each student undertakes a 6 month
industrial placement,
and some projects are sponsored by an industrial
partner throughout.
The next cohort of PhD projects with a
start date of autumn
2025
will be announced soon.
More details can be found at www.liveinno.org.
This podcast is hosted by Physics World's Katherine
Schipper. And here she is in conversation
with 2 LiveInno
directors.
Today, I'm joined down the line by Carsten
Welch and Andrea Font, who are the director
and the deputy director, respectively, of LiveInno.
And as we as we record this, it's
almost exactly a year since I submitted my
PhD thesis. And as you can tell, I
did not do a postdoc after my PhD.
And actually that's true of most of my
friends, who were in my cohort.
So
university research runs on the work of a
lot of PhD students. And today we're going
to be talking about
what the value of PhD is for students
when many of them will be at least
be considering working outside of academia.
When I think of most of the people
I know who did PhDs, a lot of
them are now data scientists,
you know, which means that they're using data
intensive analysis tools. They're using machine learning. They
might be working with AI.
And actually, even those
of my friends who I know who are
doing postdocs,
a lot of them are also doing this
data intensive science. They're doing the kind of
work that has been transformed
by AI and machine learning.
So if we're talking about the value of
doing a PhD,
I think it seems it's important to ask
whether universities are doing enough to give PhD
students those kinds of skills.
I am speaking to Carsten and Andrea today
about the topic because LiVE. InO is geared
towards students who are doing data intensive research
projects
and who are at least considering
using their scientific skills outside of academia in
industry.
So Carsten Walsh is based at the University
of Liverpool, and his research is on the
design of particle accelerators and light sources.
Andrea Font is a computational astrophysicist at Liverpool
John Moores University
who studies the formation of the Milky Way
galaxy.
Thank you so much for joining me today.
Yeah. It's great to be here.
So, Carsten, I'm gonna start with a nice
easy question. What is the purpose of doing
a PhD?
Well,
I'm not sure if that's such an easy
question. I think the the purpose of the
PhD, the primary purpose really is the to
demonstrate the ability
to conduct independent
research.
Now that sounds simple, but it's really not
a small step up from the 1st degree.
It's in fact a step that requires very
significant
skills development. And these are skills not just
in terms of additional research skills, but very
importantly, also skills
in wider professional skills.
Now also,
having said this,
I guess the ideal PhD training
should
consider more than just one possible career pathway
as you said at the beginning. So the
question that is also, how do you train
students for the career that they would like
to do?
That, I think, triggers immediately the question, what
gives us the best research? So how can
we,
get the best research out of what we
are doing? And to me, there are 3
key ingredients that are needed. 1st, it needs
an academic environment, an environment
where challenging questions can be asked freely and
where people are not afraid of thinking thinking
creatively well beyond the current status quo.
It also need access to
international and national research laboratories, so large scale
research infrastructure where people can do the big
science experiments.
And, certainly, it needs industry to basically pull
the academics back to the ground and to
reality,
to really
drive research and innovation in a way that
can benefit society.
And all these ingredients,
they have formed the training that we provide
in Live in Hope.
Right. And so at the end there, you
mentioned bringing
academics
down to the ground. But Andrea, as an
astrophysicist, you're obviously
looking,
you know, far far into the far into
the galaxy. And in in astrophysics,
you generate a a vast amount of data
from observations and simulations that you have to
analyze.
And I'm expecting that the amount of data
you generate
is only increasing.
So can you talk a bit about what
that looks like in your field and how
data intensive
tools like machine learning and AI are being
used in your field?
So, yeah, as as you said, astronomy needs
huge amounts of data.
This is a vast universe, so we need
lots and lots of data.
There is,
to give an example of a of a
current
survey, Euclid, which is recently launched
by ESA.
Euclid is
surveying
2 thirds of the sky,
of the entire sky, and it sends back
approximately 100 gigabytes of data every single night
and it will do so for the next
6 years or so. So this is just
one example of survey but there's many other
surveys out there. They're surveying the sky in
multiple wavelengths, so we have a multidimensional
data set.
So in this vast
data set, we have millions and millions of
galaxies,
objects that are moving very fast across the
sky,
like asteroids and comets in the solar system,
objects that vary in brightness, like supernovae
or stars that pulsate.
All of these objects need to be classified
and categorized.
So this is where machine learning is helping
us because,
with that huge amounts of data, it's it's
impossible for humans to do all the work.
So this is kind of the primary
example of applying machine learning in astronomy.
Okay. Thank you. It sounds like it's really
accelerating the speed at which you can do
these kinds of very data intensive analysis. Okay.
And so, Andrea, you spoke about,
some of the techniques that you're using in
your field.
And
what I'm interested to know about is whether
you think the physics community as a whole
is
adapting to the demands of
data intensive research and the new skills that
incoming PhD students are going to need to
do this kind of work.
And also
whether the physics community is adapting
enough to the fact that
most PhD students,
you know, who are doing this quite fundamental
research will nevertheless
go on to have careers outside of academia.
Yeah, that's a good point. And, I think
the physics and astrophysics communities are adapting
quite well to the new challenges. Of course,
there's a rapid
pace of development in the field.
I can speak for astronomy.
It has been estimated that the number of
publications that mentioned the word AI or machine
learning or proposed new methods in these fields
are
doubling every year and a half in astronomy
alone. So, as researchers, we need to keep
up with all this new developments.
Now how to incorporate that into training is
kind of tricky, but it's doable.
So in addition to
more traditional training that we offer in terms
of, like, courses
in computer science
and and, physics and astro.
We also,
adapt to the this fast
pace
by doing in house training with the new
methods that are they're proposed.
So,
we also,
so we could do that in variety of
ways through journal clubs, summer schools, or training
in in in partnership with our industry partners
as well.
So in addition to this, we are focusing
on,
giving the new skills and the new economy.
We're also focusing on, so what we call,
transformative skills.
We always been aware that the students
build these transformative skills in during their research.
What I'm talking here is about
communication skills, networking,
and more recently developing more entrepreneurial skills as
well in addition to the more standard data
skills.
So,
but now it's becoming more focused because the
students need to interact with industry partners so
they they actually can see for themselves how
the value of these transformative skills in the
real world. Okay, thank you. Carsten, do you
have anything to add to that?
Well, I think when it comes to how
skills have developed, the key skills that students
need, it's fair to say that this has
been an incredibly interesting journey over maybe the
past 10 years.
Personally, I had the pleasure of coordinating
6 Marie Career Networks across life sciences, physics,
and engineering,
as well as the 2 doctoral training centers
here. And I was also the chair of
SDFC's
education, training, and careers committee for several years.
And in combination, all of this has allowed
me to really think quite carefully
about what makes
the ideal training for an early stage researcher,
in particular
for PhD students, and what role do cohort
based approaches
play in this as compared to individual single
PhDs done at any university.
And I think it's it's fair to say
that today we live in one of the
most dynamic times when it comes to the
skills that are required today to do well
in research.
From changes to digital literacy,
to how we work together,
and also how we look after each other,
all of these aspects, I think, have changed
hugely over the past 5 years, and they
keep evolving at a pace where national frameworks
simply cannot follow quickly enough. So it's really
down to training initiatives like CDTs,
to pave the way for the next generation
of researchers by bringing those skills into the
training
very dynamically
when they are needed and the moment they
come up. Now one of the aspects that
has been very important in our case has
been to exploit synergies.
In Livinum, we exploit synergies all of the
time between the different SDFC research communities. We
have seen that there is benefit
in bringing nuclear and particle physicists together with
astrophysicists
and accelerator
experts, and that by doing this, everybody benefits
and that the common denominator between all of
these areas is data science. So this has
been the foundation
of Liv Inno since the start where we
have built those bridges.
But we've also
organized trainings together with other large scale training
initiatives, in particular, the Eupraxia doctoral network, which
looks after plasma accelerator research. And by bringing
PhD students from seemingly different communities together,
we have enriched everyone's training, and it has
turned out to be beneficial for everyone involved.
It this has been so successful that it
was recognized
by the European Commission as an international success
story and demonstrated
as a way forward in researcher training.
Now what have our students done, as part
of their projects?
In Livinor, every single PhD student has to
do an industry placement, so they have to
spend at least 6 months in industry working
on a topic outside of their PhD.
The aim is that they apply the skills
that they have learned during their PhD onto
a real world problem
working with another sector. That has many benefits.
It helps the other sector to solve their
problems. It really enriches the students' training, and
in particular, it also gives them a real
idea of how their career might look like
in the future. As you said at the
beginning, many students
do not end up in the academic sector.
At the same time, most students do not
really have an idea of how it is
to work in industry. So we built this
into our training, and we made sure that
the students get exposure to the industry sector
so that they can take an informed decision.
And the things that they have done in
these placements
are amazing. To just give one example, we
had a student who worked with a relatively
small company
on the data related challenges of a well-being
being platform that was established during the pandemic.
There was a platform targeting teenagers in particular
that had mental health issues, and our student
used her data skills,
to develop a chat platform to analyze the
data coming from the participants in the program
and to really inform the way forward for
that support platform.
The results were amazing for everybody,
the teenagers, our student who really loved working
in that context, and also for the company
who got somebody very skilled and talented to
work on a data challenge that they had.
Okay. Thanks. And so
you you've spoken about, you know, one of
these industry industry placements, and I'm sort of
interested in the fact that
as well as as well as, you know,
they work with industry, but actually quite a
lot of the students do what I what
I would think of as as quite fundamental
research or quite, you
know, abstract maybe astrophysical or cosmological research. So
actually, I have a question for Andrea, which
is you spoke a bit about,
at the start, about these techniques
in machine learning that you use in astrophysics.
And a lot of that was about,
about learning patterns in simulations or about analyzing
images from a telescope.
And so I'm interested
to maybe you have an example of how
a student who is doing that kind of
research that, you know, really, you know, really
pure astrophysics, maybe cosmological research might end up
using those skills in a context that is
in industry.
We have, we have a student that is,
currently using cosmological
simulations
to understand the nature of dark matter. Now
dark matter is not seen, but is everywhere.
So it's not detectable, but it's everywhere. What
is actually seen is luminous matter, which is
actually quite sparse. So it's not
the dark matter structures are not continuously
or uniformly populated with luminous matter. So it's,
in terms of statistics,
it's a sparsely populated
problem. So you have,
you're trying to get to the nature of
that matter, but you do that through the
analyzing,
sparsely populated,
data points, which are basically luminous galaxies.
So student developed techniques to,
to do research in cosmology, but turns out
that the similar techniques are actually have application
in the real world and and and, for
example, in in the Earth observation.
Now with ops with data from satellites, there's
many satellites out there that observe the Earth,
but sometimes the data is incomplete. So, again,
it's sparsely populated because of the income it
would be for the cloud coverage or any
other issues with
incompleteness of the data. So he was able
to use similar statistical methods to apply to,
the Earth observation
data
to, for example, to improve
the the landscaping,
the the
observations of the of the land to,
with the real applications into agriculture
and increasing crop efficiency, for example. So that
was a very interesting result. So you unexpected
application of cosmology
to agriculture.
So I know you're a you're a conversational
astrophysicist, but I'm I'm also guessing that if
you are
an astrophysicist, and this is maybe true in
in certain areas of accelerated physics as well,
that
and you get very good at, say, analyzing
images with AI and machine learning. That's an
incredibly in demand skill as well, isn't it,
at the moment? Yes. Absolutely. So,
as as Carson has mentioned,
there's vast applications in health care, for example,
where there's also
lots of image processing there. So we actually
have similar techniques in physics,
astrophysics,
and in other
fields including healthcare. So there's similar applications. So
this is again another point that students actually
learn
this transformative skills that I mentioned earlier. They
actually have the skills to apply to the
real world data,
but actually having the placement and doing
a separate project and applying these skills into
an entirely different field, it just opens their
eyes and opens different opportunities for them as
well, also career wise as well. Yeah. And
I have a
question for for Carsten. And this is about
I guess I guess going back again to
the philosophy
of
the CDT.
So I think
if
you're working with, say, a single supervisor, your
experience of the PhD and the way you
see yourself in your project
depends
very much on the supervisor you get. And
by that I mean whether
you are,
whether they are your boss or whether they're
sort of more like your collaborator. And I
think there's a big spectrum there.
And so how do you see the students
that come in to the CDT? What do
you think is the best way for
supervisors or people who are organizing CDTs to
view
their students? And how do
you how have you thought about that when
you've been designing these training,
this training that you give the students on
that live in OCD?
Yeah. So there's definitely not one way that
works for every student. Every arrangement is always
bespoke
and what we do is we establish a
structured career development plan at the beginning of
every PhD
project and that is a document which covers
the research, but also the training, the anticipated
workshop participation, conferences, publications, all of the things
that make a PhD. So it's quite a
complex document.
And in that document, we have a dialogue
between the student and the supervisory
team, and they write up at the start
what they think
is the most ambitious but also the most
enjoyable combination,
for the the next 3 or 4 years.
And then we take that document, and we
update it every 3 or 6 months
throughout the PhD journey.
And it turns out that every single time,
no matter how ambitious we are at the
start,
at the end, the students will have done
so much more than they ever thought possible
at the beginning.
So it's a very dynamic approach we take
to
supervision. Of course, we also have the advantage
of having several cohorts of students and quite
a large number of students, and we bring
them together on a regular basis so that
they have a chance to compare. So you
mentioned,
the the different structures that exist
between supervisors acting
more like, a line manager, say, or or
being a colleague in in the research laboratory.
And, students can compare between themselves what,
what the different approaches are, how these work.
And then in a dialogue with their supervisory
team, they can shape and reshape the way
that their own PhD project is working. And
in LIF Inno, this has worked incredibly well.
So as you were talking
there, Carsten,
what I was thinking of was
I I can imagine here from what I
remember of of working as a PhD student
that
sometimes the idea of PhD students doing placements
and having additional training that is not
immediately geared towards
learning new science for their PhD might get
some
pushback from supervisors who might think, well, why
is my student, you know, spending all this
time when they should be working for me
instead
doing, you know, doing this training that isn't
going to directly benefit my group. So actually,
I'm going to kick this question over to
Andrea.
What do you think are the benefits
for,
for academics
specifically of having students who are working on
these kinds of CBTs?
Well, there's benefits for supervisors, but also for
students as well. And I think it's benefits
for the projects in general. So what I
notice is people, students who are actually
going to placements, they they come back more
energized.
They do have,
they they learn,
how to work better in teams, how to
deliver
projects on shorter time skills that are usually
done in academia.
They come back with ideas
about how to
extend their research and make
it great spin offs or have a more
entrepreneurial
mindset,
in general, just have a broader perspective. So
I think that's very useful for both students
and and supervisors.
So every student is
doing a placement of a minimum,
duration of 6 months,
and but some placements take longer depending on
the direction of the project.
We have some cases
where, our industry partners are jointly supervising,
with with the research partners
so that,
that the placement actually,
becomes our integral part of the PhD thesis.
Vikosti, can you give an example of one
of the projects that is from the start
partnered within with an industry partner?
Yeah. Maybe maybe just one more word about
what you said earlier in terms of pushback
coming
from the students. I think it's fair to
say that when
we
first established the idea
of having a 6 months placement
integral to all of the PhD projects,
there was pushback from everybody. From the students,
from the supervisory
team, who, as you said, didn't want to
see their
student waste time
on,
research that isn't directly relevant for the PhD,
but also from the industry partners because they
felt that
they needed,
focused staff rather than students
working on their data related challenges.
Now,
that means that at the beginning, we did
have a hard time to communicate
that requirement to everybody and to get buy
into the idea, and I think what really
changed
everyone's minds was the great success that these
placements have delivered. When the students came back,
as Andrea said, they became back more energized.
The academics all of a sudden had industry
links that they would never have had before.
Academics are, most of them, not incredibly good
at establishing new links with industry partners they
have never met before, and our center just
provides these industry links automatically, so they just
have to basically be there and engage in
that,
constructive partnership. And industry all of a sudden
saw that these students,
they are actually in the 3rd or final
year of their PhD, so they are very,
very highly skilled,
and they can help them very efficiently to
solve problems.
At the same time, for industry, these placements
have also become a recruitment tool because they
have an opportunity to test somebody for 6
months continuously.
And if they like what they see, they
make them an offer. And this has happened
now several times that the students
that conducted placements were made a job offer
in the very company that they did the
placement in. And these were all companies that
they didn't have on their career
radar beforehand. They were companies that they wanted
to explore, and all of a sudden it
became so much more.
So I guess the question is then how
do we approach these companies? How do we
find the ones that we offer to our
students? And, again, that comes through dialogue with
the students. Firstly, we ask the students whether
there are maybe some companies
they would like to do a placement with.
And sometimes students would like to work with
a global player, say IBM,
Microsoft, Google to just name a few, and
then we create a link to these companies
and see what opportunities there are. Sometimes they
also
would like to explore a much smaller company
in the region because they see that as
a potential employment opportunity.
And then, again, we do help to create
that link.
And, we do, of course, also have a
very long list of companies that is now
connected with our CDT where we can also
make proposals of what we think is a
good fit to our students' interest. Now if
I can give one specific example of a
journey that we had with a company, it's
a relatively small company,
called Adaptics,
an SME that is specialized in 3 d
imaging technologies.
And we started working with them maybe 10
years ago. And at the beginning, it really
was,
a a very,
simple partnership. We had some computational
and modeling skills that we thought would be
beneficial for them,
fundamentally, using Monte Carlo based approaches to model
radiation impacting
on the skull of a patient and then
seeing,
what kind of intensity distributions we get, how
we can understand errors and error propagation and
these kind of things. Now that has developed
fantastically over that period. It started with just,
a little bit of overlap. We then had
a first PhD student, which was funded by
the university
working on an industry related,
problem that Adaptics had. Then we continued the
journey in Livinnow by now having a student
who is jointly funded 5050
between the company
and the center.
And very recently,
in fact, just a few weeks ago, we
were now awarded funding by SDFC
for a half a million pound project that
will take the technology that we have jointly
developed to the next level. So it's really
a success story that shows that we
are prepared to engage with industry
at a very small level initially. We want
to show that we are serious about this.
We want to show that the students can
really work extremely well with that business. And
then if if all the sides see the
advantages
that are in it for them, then automatically,
almost, this will grow over time.
And as you mentioned, the CEC has been
running
in in some iteration
for for a while now. When it when
you first started, was it difficult
to approach,
industry and sort of make the case that
these companies should have,
a student work with them for 6 months?
Yeah. It was very difficult, and we had
to spend considerable time. I had personally to
spend considerable time,
talking with businesses, explaining the skills our students
had, explaining why
I thought, why we thought that it would
be beneficial to put them onto their
ongoing research projects.
And, what really then changed the mindset of,
companies subsequently
were the success stories that we had from
these initial,
companies and the fantastic results that came out
of our research.
Now can you talk a bit about what,
what's in store for the future of,
of the CBC?
Yeah. I think it's fair to say that
we have come a long way over the
last 10 years. And of course we would
like to continue our successful journey and and
offer more students the opportunity really
to be trained in the LiveInno way as
you might call it.
So we we do hope that we can
get additional studentships funded from STFC and other
funders but also from external partners, research centers
and industry. We've really seen that this way
of training students has worked incredibly well
so we really would like to continue that
journey with our partners. In terms of research,
I think what we've seen in the past
2 years is an ever stronger focus on
AI,
which is becoming increasingly important for all of
our research areas.
And, I think we are well positioned
for tackling the research challenges in this area
across all of our different communities.
For example, we've just recruited a new lecturer
at the University of Liverpool who now has
a focus on AI for
particle accelerator. So also in terms of the
academic talent,
we have really positioned us well for the
challenges of the future.
And are there any,
I guess, upcoming
if someone is listening to this and that
either, you know, someone who
works in industries interested in getting involved or
is,
you know, say,
an undergraduate student who thinks this sounds like
something they're interested in, are there any upcoming,
deadlines that they should be aware of?
Yeah. So every year, we have an open
call to academics in the departments of physics,
mathematics, computer science, engineering, and we ask them
for their data science PhD projects. And then
there is a prioritization
process.
And typically at the end of October, early
November, we publish
these projects on our website, and then there's
a first application deadline at the end of
January. So I would encourage everyone who is
interested in doing a PhD within LIFINTO to
have a careful look at our website when
these projects are released later this year.
The first version of ChatCBT, that would have
come out in oh, it's 2021.
So this would have been yeah. Obviously, while
the CDC was running, did you did you
clock that when it happened? Because for me,
that's sort of the
the start of these, like,
models that I think actually a lot of
people use for their in their research now
becoming a really big thing. Did you did
you clock that and you think, oh, this
is something that is gonna really change the
way that our students work.
We we did, in 2 different ways. On
the one hand, of course, we all of
a sudden needed to really put some emphasis
on
teaching our students about prompt engineering,
so so how to really use those tools
efficiently.
But maybe
and maybe even more importantly,
we also have to teach them about the
limitations,
very fundamental limitations of these tools,
because if wrongly
applied or applied for
the wrong questions, they can be completely wrong.
And I think today is one of the
big challenges is to really distinguish,
between fact,
and
generated
soil knowledge,
where tools suggest that they give you a
very nice and rounded text, but where if
you look in the details, which is particularly
relevant,
in in research,
they are quite fundamentally wrong. And learning this,
learning how to deal with that and learning
where to find quality information
is really something,
that gets stronger a stronger and stronger focus
in our training.
So the students on the CVC at the
moment, they're gonna finish in 2028.
Right? Yep. That's correct. Right. And I can
imagine that if you if you think of
how quickly, for example, something like ChatCBT has
evolved, I can imagine that
by the time they finish,
the landscape around AI and machine learning is
gonna
look quite different. Are you kind of keeping
an eye on that? Are you anticipating that
you might have to sort of
maybe adapt the CDT over the next couple
of years as that stuff changes?
Yeah. We definitely have to adapt.
And as you you're right, we say AI,
driven tools like ChatGPT
are becoming increasingly important. And I guess that's
one of the strengths of our
approach of using that structured career development plan
where, by definition, every 3 to 6 months,
the supervisory team sits down with the student,
and they look at every aspect of the
project, including
the training and the skills that the student
needs. And then if centrally we realize that
there is now a big push in a
new area that simply didn't exist,
a few years ago, then we build this
into the training program. We either
train our own experts or we work with
external specialists,
and then we bring those skills to the
students and make sure they are positioned best
for their future careers.
Thank you so much for joining me. You've
been listening to,
Carson Walsh and Andrea Fonte of the LIV
Inno Centre For Doctoral Training in Liverpool.
Thank you so much.
I'm afraid that's all the time we have
for this week's podcast.
Thanks to Andrea Fonte,
Carsten Welsh, and Katherine Skipper for a fascinating
conversation,
and a special thanks to our producer, Fred
Ailes.
This episode is sponsored by LiveInno,
the Liverpool Centre For Doctoral Training For Innovation
in Data Intensive Science.
The UK based CDT
is a partnership between the University of Liverpool
and Liverpool John Moores University.
Since 2022,
LiveInno has offered 3 cohorts
of students
4 year fully funded
PhD
studentships
in data intensive science.
From medical physics to quantum computing,
the CDT has a diverse portfolio
of research projects,
but students all share training in high performance
computing,
machine learning, and AI, and data analysis.
The CDT
also gives students access to career advice and
training in project management,
entrepreneurship,
and communication
skills.
As well as working on their original research,
each student undertakes a 6 month
industrial placement, and some projects are sponsored by
an industrial partner throughout.
The next cohort of PhD projects with a
start date of autumn
2025
will be announced soon.
More details can be found at www.liveinno.org.