Open position at Artificial Intelligence Center FEE CTU
Ph.D. in Structured Graphical Models (ML for Public Health)
- Work schedule
- Karlovo nám. 293, 120 00 Praha-Nové Město, Česko
We seek a Ph.D. student with a background in
computer science, statistics, or applied mathematics to work on an exciting Horizon Europe project. The research will involve developing new probabilistic graphical models, proving statistical properties thereof, developing techniques to adequately train them with strong accuracy and generalization, and testing them on real biomedical and
public health data.
Structured Graphical Models for Causal Inference is increasingly an important class of Machine Learning techniques, especially in applications of public health and medicine. Using careful well-principled structure in the modeling, conclusions can be reached regarding the cause-effect, rather than simply correlational influence, with far reaching implications for understanding and treatment/policy.
The development, analysis, training, and inference of these models are highly nontrivial and involve an array of tools from statistics and information theory along with optimization and numerical analysis. As part of a Horizon Europe EU-wide project, we are seeking motivated and competent Ph.D. students to pursue research in the associated fields.
You will work on a project which is coordinated by Jakub Mareček, Ph.D. who has worked in two start-ups, IBM Research Ireland, and at the University of California LA, the University of Edinburgh, and the University of Toronto.
As the Head of the Optimization group in the AI Center, he designs and analyses algorithms for optimization and control problems across a range of application domains, including power systems, quantum computing, and robust statistics.
Academics can see further into the future and thus can help solve tomorrow‘s problems of any industry. Nowadays we exclusively work with companies that want to see the unseen, that are interested in what the next 10 or 20 years will bring.
Salary and benefits
The position is full-time and limited to 3 years, but can be extended in case of mutual interest.
The studentship comes with a monthly salary adjusted to approximately match the average salary in Prague, the Czech Republic (which currently stands at approximately EUR 23 000 p.a. before a notably low tax).
You are entitled to 6 weeks of paid vacation which you will find useful when exploring the beautiful parts of our country including a number of UNESCO landmarks.
Get excited about a travel budget for attractive conferences abroad, regular internal seminars, an unlimited supply of coffee (very appreciated).
Our labs are located on the university campus in the historical city center with a view of Prague Castle, so perfect transport accessibility is another obvious advantage.
The Czech capital regularly ranks among the five best Eropean cities to live in.
What we appreciate the most about our research center is its inspirational and supportive environment. We love to cooperate across teams and benefit from our diverse research network.
It‘s interesting that there are professors known all over the world and yet they don‘t give you the feeling they are something more. They come to your presentation, take interest in your problem at the seminar and try to help you.
Master's degree in Computer Science, Mathematics, Statistics, or related disciplines.
A preference is given to candidates with experience in programming.
How to apply
- Send your structured CV and· motivation letter to our HR Manager firstname.lastname@example.org or via the form below.
- We will contact you in approx. two weeks whether you qualify for the position and if we think you would be a good fit for our team.
- You will then get an interview with our Optimization researchers.
- If you agree, we will invite you for a visit to give a talk at our internal seminar. You will meet our team, spend a day with us in meetings, have lunch and we will show you around.
- Shortly after, you will get a final answer and we will decide on a suitable starting date.
Zakládáme si na inkluzivite a diverzite. Chceme mít lidi po hromadě lidi, kteří jsou skvělí v inkrementálním výzkumu Opakem jsou moonshoty - exponenciální výzkum - namyslíte, kde může být daný problém za 10 let. Pro špičkové výsledky se snažíme kombinovat oba přístupy.