Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
of small cryptic plasmids in the development and spread of antibiotic resistance, and ii) Use machine learning tools to examine the complex interplay between bacterial hosts, various plasmids and resistance
-
for Quantum Technology (WACQT, http://wacqt.se ). The core project of the centre is to build a quantum computer based on superconducting circuits. You will be part of the Quantum Computing group in the Quantum
-
2D materials, coatings, nanomaterials, and metal machining. A key focus is improving the sustainability of AM by studying powder degradation and enhancing powder reuse and recycling. We are now seeking
-
and accepted to the PhD program at Stockholm University. Project description Project title: “Deep learning modeling of spatial biology data for expression profile-based drug repurposing”. A new exciting
-
well as the clinical activities at the Karolinska University Hospital, unique access to international expertise in machine learning, state-of-the-art imaging, diverse patient cohorts, and relevant computational
-
academia and industry. Requirements The following qualifications are required: Solid knowledge in mathematics and statistics, in areas such as linear algebra, probability theory, machine learning, high
-
development (using both traditional signal processing and machine learning), antenna design, and system hardware development. We collaborate closely with clinical experts to develop innovative technologies
-
particularly valuable. Documented experience with machine learning and biostatistics is also highly meritorious.You can find information about education at postgraduate level, eligibility requirements and
-
Join the cutting-edge RAM³ project: Unlocking the Potential of Recycled Aluminium through Machine Learning, High-Throughput Microanalysis, and Computational Mechanics. We are offering a PhD position
-
effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable