Education
An important part of eSSENCE's mission is to promote the use of e-Science methods, This is done, among other things, through a graduate school.
Graduate school for data-intensive science
eSSENCE in collaboration with SciLifeLab and STandUp runs an interdisciplinare scool to adress challanges in data-intensive science.
The primary mission is to ensure a long-term foundation for high-profile research in areas critical to other strategic initiatives in data-driven science at partner universities and nationally. The School will be an arena where experts in computational science, computer science and engineering, i.e. systems and methodology, work closely with researchers in the data-driven sciences, industry and society to accelerate data-intensive scientific discovery. The School should actively work to create synergies between the relevant strategic research initiatives, complement related strategic initiatives at the University and nationally, and actively encourage collaboration with industry and society.
The Graduate School is run on the initiative of eSSENCE and in collaboration with SciLifeLab and STandUp. The education program is run in collaboration with SeSE.
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Graduate school, Uppsala University. Photo: Mikael Wallerstedt
PhD students
These PhD students at Uppsala University are part of the eSSENCE graduate school in data-intensive science.
Currently, Max Kovalenko, Viktor Svahn and Inga Kristin Wohlert form the schools activity group.
Yaqi Alexandra Deng
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Neural Networks in Precision Health Applications and for Gene-Based Association Tests
Department of Immunology, Genetics and Pathology
Max Kovalenko
Novel Methods for Deep Learning in Genetic Epidemiology
Department of Information Technology
Love Nordling
Oncospace - multispectral 3D-analysis of immune cell interaction in the lung cancer microenvironment for patient specific cancer therapy
Department of Immunology, Genetics and Pathology
Vaishnavi Divya Shridar
Towards model-based analysis in wastewater epidemiology: the specifics of antimicrobial resistance
Department of Information Technology
Viktor Svahn
Accelerated data-intensive e-Battery research
Division of Structural chemistry
Elise Jonsson
SWEFE-NEXT: The Swedish Water-Energy-Food-Ecosystem Nexus and its response to hydroclimatic EXTreme events
Department of Earth Sciences
Simon Barton
Bayesian Machine Learning for Astronomical Object Classification
Department of Physics and Astronomy
Patrick Hennig
Data-driven assessment of cell perturbations using live-cell imaging and automated microscopy
Department for Pharmaceutical Biosciences
Zhenlu Sun
Data-driven Vulnerability Analysis for Critical Infrastructures
Department of Information Technology
Inga Kristin Wohlert
Data-intensive detection and characterisation of online AI-based coordinated behaviours
Department of Information Technology
Jack Rose-Butcher
Data-driven predictability of nonlinear dynamics of offshore renewable energy systems to enhance performance and survivability
Department of Electrical Engineering
Jiawei Li
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Digital Biomarkers from the Electrocardiogram using Artificial Intelligence
Department of Information Technology
Jeremie Sefo
Data-driven methods for inverse scattering and wave-focusing with applications in medical ultrasound
Department of Information Technology
Sajjad Rahmani Dabbagh
Image Analysis and Artificial Intelligence in Mass Spectrometry Imaging
Department of Information Technology
Léo Bechet
Energy-Efficient Algorithmic SolutionsDriven by Data-Intensive Applications
Institutionen för informationsteknologi
Matevz Turk
Deciphering connections between structure and Li+conductivity in composite solid-state electrolytes
Department of Chemistry Ångström