Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- KTH Royal Institute of Technology
- Linköping University
- Lunds universitet
- KTH
- SciLifeLab
- IFM, Linköping University
- IFM/Linköping University
- Karolinska Institutet (KI)
- Lulea University of Technology
- Nature Careers
- SLU
- Sveriges Lantbruksuniversitet
- Umeå universitet
- University of Lund
- Uppsala universitet
- 6 more »
- « less
-
Field
-
–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
-
. The position bridges machine learning and molecular science, with opportunities for collaboration, mentorship, and impactful research. About us The Department of Computer Science and Engineering (CSE
-
, and demonstrated ability to develop computational pipelines for biological datasets. Experience in statistical modeling and/or machine learning applied to biological systems, with the ability to link
-
statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft matter and nanomaterials
-
/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle delivery. The postdoc will join Professor Nathaniel R. Street’s team at UPSC, working closely with
-
testing and collaboration with infrastructure owners or managers - Experience in supervision - Knowledge of data-driven methods, signal processing, or machine learning - Familiarity with sustainable
-
the last three years prior to the application deadline. Experience in some of the following areas is meritorious: AI and machine learning; convex analysis; functional analysis; mathematical statistics
-
Description of the workplace The position will be placed at the division of Computer Vision and Machine Learning at the Centre for Mathematical Sciences. The Centre for Mathematical Sciences is an
-
at the intersection between analytical chemistry, chemometrics and life sciences. As a postdoc in this project you will learn to use and help to develop cutting-edge methodologies linked to vibrational spectroscopy and
-
data using multivariate statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft