-Masters degree in Statistics (or a related
field), Computer Science,or be close to its completion.
-Applicants must have an excellent academic record.
Skills:
-Background and experience in machine learning and statistics.
A track record in quality research, as evidenced by publications in scientific
journals and conferences of the field.
-Self-motivated, independent researcher with scientific curiosity and honesty.
-Demonstrated ability to work independently and as part of a collaborative
research team.
-Good interpersonal skills, with ability to present and publish research
outcomes in spoken and written form.
-Fluency in spoken and written English.
The preferred candidate will have:
-Familiarity with statistical modeling, machine learning and approximate
inference.
-Expertise with generative modeling, stochastic processes and approximate
inference.
-Familiarity with reinforcement learning concepts, if interested in working on
the design of sequential decision algorithms.
-Solid programming skills in Python, experience with PyTorch/JAX would be
idea
The projects goal is to research and develop novel statistical machine
learning and inference methods for predictive and preive tasks that overcome the
challenges posed by applied settings (e.g., non-stationary and missing
not-at-random phenomena), combining probabilistic models, deep learning, stochastic
processes and approximate inference.
