Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- KTH Royal Institute of Technology
- Lunds universitet
- University of Lund
- Chalmers tekniska högskola
- KTH
- SciLifeLab
- Swedish University of Agricultural Sciences
- IFM, Linköping University
- Linköping University
- Linköping university
- Linköpings universitet
- Nature Careers
- SLU
- Sveriges Lantbruksuniversitet
- Umeå universitet
- Umeå universitet stipendiemodul
- Uppsala universitet
- chalmers tekniska högskola
- 9 more »
- « less
-
Field
-
. 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
-
/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
-
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
-
data using multivariate statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft
-
statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft matter and nanomaterials
-
mathematics, optimisation, biomathematics, statistics, and data science with strong connections to engineering, natural sciences, and industry. About the research project We will recruit one postdoc in applied
-
science, machine learning, automated systems, or a closely related field Have experience working with ruminants Have experience in precision agriculture and/or precision livestock farming Have experience
-
imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring
-
–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
-
risk factors. The main objective is to design and apply machine learning and deep learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include