PhD student / student scholarships in Jagiellonian University

We are looking for Ph.D. students and final-year postgrad students in computer science or mathematics who are eager to contribute to advancements in Bayesian Neural Networks. If you are passionate about Bayesian Deep Learning and want to make a difference, please visit our website at bayes.net.pl and don't hesitate to email us at [email protected] !

Scholarships

We offer scholarships for students and doctoral candidates in Poland. The scholarship amounts to up to PLN 5000 (approximately EUR 1250), raising the total doctoral student remuneration to PLN 8460 (approximately EUR 2000). This attractive offer is particularly appealing given the affordable living costs in Poland.

Work specification

A successful candidate will work in the field of Bayesian Deep Learning on a project funded by the National Science Center (European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement) titled Balancing Priors and Learning Biases to Improve Bayesian Neural Networks. The research will be conducted within the Group of Machine Learning Research ( at the Jagiellonian University. Your role will involve theoretical investigations, model implementation, and experiments focusing on learning and priors for Bayesian Neural Networks . Key topics include uncertainty quantification, priors and posteriors, amortized and boosted posterior models, and deep networks architectures .

Process for new doctoral candidates

The recruitment process for new doctoral candidates consists of two stages:

• First, contact the Principal Investigator (PI) at [email protected] 
• Second, apply to the doctoral school at the Jagiellonian University. The registration deadline is June 17, 2024 (Technical CS recruitment schedule: or alternatively June 23, 2024 (CS recruitment schedule:

Please attach the following documents to your e-mail:

• CV/Resume 
• Motivation or cover letter explaining how you can contribute to the project 
• Any relevant links or materials demonstrating your skills and knowledge 

We are looking for Ph.D. students and final-year postgrad students in computer science or mathematics who are eager to contribute to advancements in Bayesian Neural Networks. If you are passionate about Bayesian Deep Learning and want to make a difference, please visit our website at bayes.net.pl and don't hesitate to email us at [email protected] !

Scholarships

We offer scholarships for students and doctoral candidates in Poland. The scholarship amounts to up to PLN 5000 (approximately EUR 1250), raising the total doctoral student remuneration to PLN 8460 (approximately EUR 2000). This attractive offer is particularly appealing given the affordable living costs in Poland.

Work specification

A successful candidate will work in the field of Bayesian Deep Learning on a project funded by the National Science Center (European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement) titled Balancing Priors and Learning Biases to Improve Bayesian Neural Networks. The research will be conducted within the Group of Machine Learning Research ( at the Jagiellonian University. Your role will involve theoretical investigations, model implementation, and experiments focusing on learning and priors for Bayesian Neural Networks . Key topics include uncertainty quantification, priors and posteriors, amortized and boosted posterior models, and deep networks architectures .

Process for new doctoral candidates

The recruitment process for new doctoral candidates consists of two stages:

• First, contact the Principal Investigator (PI) at [email protected] 
• Second, apply to the doctoral school at the Jagiellonian University. The registration deadline is June 17, 2024 (Technical CS recruitment schedule: or alternatively June 23, 2024 (CS recruitment schedule:

Please attach the following documents to your e-mail:

• CV/Resume 
• Motivation or cover letter explaining how you can contribute to the project 
• Any relevant links or materials demonstrating your skills and knowledge