职位信息
PhD student in Machine Learning with a focus on probabilistic models in microbial and cellular ecology
2024年12月31日截止
All ecological systems, including ecologies of cells, consist of interacting parts. Cell imaging data, which hold rich information about how cells act and interact with each other, can be translated into spatio-temporal point patterns in which points represent cell locations.
In this project, the doctoral student will develop mathematical models and machine learning methods that probabilistically infer information about microbial and cellular ecologies from the point patterns that they generate.
During the process, the student will learn how to combine mathematical models (based e.g., point processes and differential equations) with machine learning methods (e.g., Hamiltonian Monte Carlo sampling and neural networks) to solve research problems in microbial and cellular ecology.
The doctoral student will join the Artificial Intelligence and Mathematics for Oncology (AIMOn) group and work closely with experts in applied mathematics, statistics, machine learning and experimental cell and microbiology. The project is funded by a grant from the Swedish Research Council.
To meet the entry requirements for doctoral studies, you must:
hold a Master’s (second-cycle) degree in engineering physics, applied mathematics, physics, computer science, machine learning, or in a similar research field, or
have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including an independent project worth at least 15 credits, or
have acquired substantially equivalent knowledge in some other way.
We are looking for candidates with:
a strong interest in developing probabilistic mathematical models and machine learning methods to understand microbial and cellular ecologies,
good communication skills and sufficient proficiency in oral and written English,
excellent study results,
programming experience,
creativity, thoroughness, and a structured approach to problem-solving,
a collaborative mindset and enthusiasm for interdisciplinary work.
A Ph.D. student is expected to devote their time to graduate education mainly. The rest of the duties may involve teaching at the Department, including also some administration, to at most 20%.
More information please contact:
https://uu.varbi.com/en/what:job/jobID:771410/
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@留德华叫兽：系美国Clemson大学数学硕士(运筹学方向)、Ph.D. candidate，欧盟玛丽居里学者，德国海德堡大学数学博士(离散优化、图像处理)，读博期间前往意大利博洛尼亚大学、IBM实习半年，巴黎综合理工访问一季。现任德国无人驾驶资深研发工程师。
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