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Science & Technology 🏢 Contract ⭐️ Terverifikasi

Research Officer, Computational Sciences (AI focus), BII

Agency for Science, Technology and Research
Singapore
Estimasi Gaji
SGD 54.000 – SGD 90.000
Live Update
12 Juli 2026
Batas Akhir
12 Jul 2027

Deskripsi Pekerjaan

Are you a pioneering computational scientist with a passion for leveraging Artificial Intelligence to solve complex biological challenges? The Agency for Science, Technology and Research (A*STAR) invites highly motivated individuals to join its cutting-edge Bioinformatics Institute (BII) as a Research Officer, Computational Sciences (AI focus). This is a unique opportunity to contribute to groundbreaking research that has the potential to redefine antimicrobial strategies and impact global health.

In this dynamic contract role, you will be instrumental in unravelling the intricate mechanisms of surfactant-membrane interactions. Your expertise in advanced computational modelling, coupled with a strong foundation in AI and Machine Learning, will drive innovation, providing critical insights that underpin the development of novel antimicrobial solutions. This position is ideal for a proactive researcher who thrives in an interdisciplinary environment, collaborating with experimentalists and fellow computational experts to bridge theoretical understanding with practical applications.

As part of A*STAR’s BII, you will operate within a world-class research ecosystem, equipped with state-of-the-art computational resources and a vibrant community dedicated to scientific excellence. If you possess a keen analytical mind, a drive for discovery, and the ambition to make a tangible difference in biomedical science through computational innovation, we encourage you to apply. Join us in Singapore and contribute to the next generation of antimicrobial breakthroughs!

Tanggung Jawab

  • Conduct advanced computational modelling and simulations to investigate surfactant-membrane interactions.
  • Design, implement, and apply state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) algorithms for complex biological data analysis and predictive modelling.
  • Collaborate closely with experimental scientists to integrate computational predictions with laboratory findings, guiding experimental design and interpretation.
  • Analyze large-scale biological datasets, utilizing bioinformatics and statistical tools to extract meaningful insights.
  • Prepare and present research findings effectively in internal meetings, scientific conferences, and high-impact peer-reviewed publications.
  • Actively contribute to the development of grant proposals and research project initiatives.
  • Maintain and develop robust computational pipelines and workflows for scientific investigations.

Kualifikasi

  • Master's or PhD degree in Computational Biology, Computational Chemistry, Computational Physics, Computer Science, Bioinformatics, or a closely related quantitative field.
  • Demonstrated experience in molecular dynamics simulations, coarse-grained modelling, or similar computational biophysics/biochemistry techniques.
  • Strong theoretical understanding and practical application experience in Artificial Intelligence (AI) and Machine Learning (ML) methodologies.
  • Proficiency in programming languages such as Python, R, C++, or Julia, with experience in scientific computing libraries (e.g., NumPy, SciPy, TensorFlow, PyTorch).
  • Familiarity with high-performance computing (HPC) environments and cluster management.
  • Excellent analytical, problem-solving, and critical thinking abilities.
  • Proven ability to work independently, manage research projects, and collaborate effectively within a multidisciplinary team.
  • Strong written and verbal communication skills, capable of articulating complex scientific concepts to diverse audiences.

Keahlian yang Dibutuhkan

Computational Biology Artificial Intelligence Machine Learning Molecular Dynamics Biophysics Biochemistry Bioinformatics Python R C++ HPC Data Analysis Scientific Computing Antimicrobial Discovery Surfactant-Membrane Interactions Statistical Modeling Predictive Modeling Research Innovation

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