35 postdoc-in-thermal-network-of-the-physical-building research jobs at University of Adelaide in Australia
Sort by
Refine Your Search
-
: Safe AI in the Real world, working on foundational reasoning for tool use and physical interactions. Theme 3: Diverse AI, working on AI systems for safety and diversity. Theme 4: AI that can explain its
-
responsible for the delivery of high-calibre farming systems research, based on a network of already established field trials to evaluate how crop rotation/sequence, time of sowing and nitrogen management
-
decision-making. You will play a key role in both supporting and leading the delivery of farming systems research, based on a network of already established field trials to evaluate how crop rotation
-
contribution of 17% superannuation applies. Fixed term position for 24 Months. Postdoc opportunity - Open for Applications Until Filled. We are seeking a dynamic and motivated Postdoctoral Fellow in Ecological
-
and MALDI/ESI, and lignin composition using TDA/GCMS. The postdoc will work closely with teams developing engineered plants to develop a deeper understanding of cell wall architecture. This is expected
-
membership is an EMBL Partnership Programme established in 2010, entitled ‘EMBL Australia Partner Laboratory Network’ (PLN). The partnership aims to seed a dynamic, highly collaborative culture across
-
focused on understanding and countering harmful narratives and, mis/disinformation, and applying social network analysis. To be successful you will need: PhD in a relevant discipline such as computer
-
proteins, combined with methods such as microscale thermophoresis will also be used to characterise enzyme activity. The postdoc will also co-supervise PhD students and Honours students. Outputs will include
-
to solve problems of global significance. Learn more at: set.adelaide.edu.au How to Apply Join the Australian Plant Phenomics Network (APPN) and make a real difference in sustainable agriculture across
-
experienced Postdoctoral Research Fellow B to undertake research in the field of physics-informed neural networks for magnetic sensing. This project combines the strengths of industry and academia to leverage