Job Description

We are looking for a dynamic, enthusiastic, and resourceful Research Fellow with knowledge and research skills in the area of ageing, machine learning, chronic kidney disease, to join the team.

The selected candidate will perform a variety of research activities within the overall scope of a research project under the supervision of the Principal Investigator and/or his designate, including but not limited to the following: 
Independently carry out the design and execution of experimental research required by the research component of the project.

• Perform analysis of complex and large-scale data, including (but not limited to) peptide design and/or in silico testing WGS and transcriptomic data (including single cell sequencing and spatial transcriptomics data).
• Use computational tools and algorithms written in one/all R, PERL and Python.
• Application of qualitative and quantitative research techniques which include accurate in-depth assessment, interpretation, and evaluation of machine learning, -omics data sets, conceptualise new ideas and develop plans for independent research in the field of de novo peptide design, in silico protein-protein interaction,  WGS and other -omics data modalities (e.g., RNAseq, scRNA-seq, spatial transcriptomics etc.).
• Write and review research papers, present research outcomes and develop connections with local and international researchers for collaboration work.
• Contribute to project management, provide guidance to junior researchers as well as undergraduate and graduate students, occasional educational/instructional activities.
• Perform other related duties incidental to the work described herein.

Job Requirements

• PhD in a related scientific area (e.g., Machine Learning, Bioinformatics, Computational Biology, Data Analytics) with demonstrated hands on experience in bioinformatics and genetics/genomics data analyses. Candidates with higher credentials may be considered for more senior appointments.
• Demonstrate very good knowledge, skills and expertise, with potential to achieve research excellence in genetics and genomics.
• Experience in data analysis and statistical modelling of high throughput screening data, genomic data, as well as in network analysis and application to complex diseases.
• Familiar with high-performance computing (HPC) environment and possesses strong computational biology skills (experience in R, Perl, Python, SQL, Bash etc).
• Proven track record in the form of high impact publications.
• A team player who is able to prioritise, multi-task and work collaboratively in a diverse workforce.

We regret that only shortlisted candidates will be notified.