In this case study, we hear from Shuo Zhang, a PhD student in Computer Science, who is working on predicting protein properties using sequence information. Our work, SeqProFT, is a smart new way to predict protein properties using sequence information alone. Using BlueBEAR GPU resources, we trained and evaluated SeqProFT across a wide range of … Continue reading “SeqProFT: Predict Protein Functions Using Less Computing Power”
Category: Case study
June 2026 BEAR Newsletter
Birmingham Environment for Academic Research (BEAR) Newsletter Last week we were excited to welcome 60 undergraduate and taught postgraduate students to BEAR Challenge 2026! Supported by many members of the ARC team, they experienced High Performance Computing (HPC) for the first time and heard about career opportunities in this field. The successful event was part of … Continue reading “June 2026 BEAR Newsletter”
Exploring the dynamics of alien solar systems with BlueBEAR
In this case study, we hear from Alix Freckelton, a PhD student in Physics and Astronomy, who is exploring transit timing variations (TTVs). My research focuses on transit timing variations (TTVs): these are tiny irregularities in the times that we see exoplanets transiting their host stars. TTVs are caused in multi-planet systems by the planets … Continue reading “Exploring the dynamics of alien solar systems with BlueBEAR”
AlphaFold protein structure prediction to study DNA replication
In this case study, we hear from Paolo Passaretti, a postdoc in Cancer and Genomic Sciences, who is investigating how cells copy and protect their genomes during DNA replication. I am a postdoctoral researcher in Prof Aga Gambus’ Laboratory, in the Department of Cancer and Genomic Sciences, where we study how cells copy and protect … Continue reading “AlphaFold protein structure prediction to study DNA replication”
De Novo Protein Engineering: Future-Proofing MRI Contrast Agents with BlueBEAR
In this case study, we hear from Sarah Berger, a researcher working in Bioinformatics, who has been using BlueBEAR to develop proteins that can bind to metals. Magnetic resonance imaging (MRI) is an essential diagnostic tool, particularly powerful when combined with contrast agents. Most state-of-the-art MRI contrast agents are based on the metal gadolinium, which has intrinsic physical … Continue reading “De Novo Protein Engineering: Future-Proofing MRI Contrast Agents with BlueBEAR”
May 2026 BEAR Newsletter
Birmingham Environment for Academic Research (BEAR) Newsletter Members of the team attended a great BEAR PGR Conference at the end of April on the theme of AI, research integrity and ethics. There were some really thought-provoking talks on the implications of AI and some interesting examples of PhD research from across the university. The event was organised … Continue reading “May 2026 BEAR Newsletter”
Using AI to Bring Indigenous Storytelling Character Figures into Chemistry Education
In this blog, we hear from Dina, a second-year Ph.D. student in Chemical Education, School of Chemistry, who won the best short talk at the BEAR conference 2026. Dina, conducted research that intersects chemistry education, technology, and ethnochemistry. Ethnochemistry is a multidisciplinary field that explores the relationship between chemistry and the traditional wisdom, practices, and … Continue reading “Using AI to Bring Indigenous Storytelling Character Figures into Chemistry Education”
Learning from Nature: Using HPC to Design Efficient Jet Propulsion
In this case study, we hear from Yukesh Karki, a doctoral student from Aerospace Engineering in the School of Metallurgy and Materials, who used BlueBEAR to identify the optimal three-dimensional nozzle geometry for hydrodynamic jet propulsion. He is also the winner of ‘best talk’ at the BEAR Conference 2026. Nature has already evolved highly efficient methods … Continue reading “Learning from Nature: Using HPC to Design Efficient Jet Propulsion “
Research project: Exploring Julia for High-Performance Computing
This project was initiated and led by Dr Vincenzo Brachetta and carried out by PhD researcher Ritesh Moon at the University of Birmingham. Conducted between January and February 2026, it evaluated the performance and practical usability of Julia on BlueBEAR, the University’s institutional high-performance computing (HPC) platform. BlueBEAR supports multi-core CPU workloads, distributed execution across nodes, and GPU-accelerated computation, with … Continue reading “Research project: Exploring Julia for High-Performance Computing”
Understanding multi-agent learning through large-scale simulations
In this case study we hear from Tuo Zhang, a researcher working in Machine Learning, who has been using BlueBEAR to run large-scale simulations of multi-agent reinforcement learning (MARL) algorithms. His research focuses on understanding how learning agents behave when interacting with each other in complex and changing environments. In many real-world systems, decision-making is not performed … Continue reading “Understanding multi-agent learning through large-scale simulations”