寡核苷酸计算设计高级研究员(J24727)

Responsibilities

• Lead Oligo Design: Drive the hands-on computational design of synthetic oligonucleotides, applying advanced sequence analysis and bioinformatics to select optimal targets, maximize on-target efficacy, and stringently minimize off-target risks.
• Bioinformatics & Data Analysis: Analyze large-scale biological datasets (including bulk/single-cell RNA-seq, transcriptomic and genomic data) to inform target selection, alternative splicing strategies, and sequence optimization.
• Pipeline Development: Design and implement automated bioinformatics, data processing, and visualization pipelines to support high-throughput oligonucleotide design and performance analysis.
• AI/ML Application: Develop and apply machine learning models to explore novel chemical space, predict oligo performance across diverse RNA targets, and prioritize candidates for experimental testing.
• Cross-Functional Modeling: Build integrative models that combine sequence, secondary structure, and oligo chemistry. Partner with our in-house Molecular Dynamics (MD) team to incorporate structural and mechanistic insights into the design loop.
• Wet-Lab Collaboration: Collaborate closely with oligonucleotide chemists, molecular biologists, and pharmacologists to embed computational insights into experimental workflows and advance candidates toward development.

Required Skills

• Education: PhD in Bioinformatics, Computational Biology, Computational Chemistry, or a closely related discipline.
• Experience: 5+ years of hands-on experience in bioinformatics and computational oligonucleotide design, ideally in an industry setting.
• Oligo Design Expertise: Proven, hands-on track record in the rational design of RNA-targeted therapeutics (ASOs, siRNAs, etc.). Experience incorporating nucleotide modifications (e.g., 2’-O-methyl, phosphorothioate, LNA or related chemistries) into computational design strategies to optimize stability and target engagement is preferred.
• Bioinformatics Mastery: Deep expertise in genomics and transcriptome analysis workflows, sequence alignment algorithms, RNA secondary structure prediction tools, and off-target profiling methodologies.
• Programming & AI/ML: Strong programming skills in Python and R, with experience using scientific computing libraries and AI/ML frameworks (such as TensorFlow or PyTorch) on biological datasets. Experience with version control (Git) and reproducible research practices.
• RNA Biology: Solid understanding of RNA biology, including alternative splicing, RNA processing, RNA editing, and RNA-binding proteins, and how these influence therapeutic design.
• Collaborative Mindset: Proven ability to work within multidisciplinary teams. Note: While familiarity with physics-based modeling (Molecular Dynamics) is a plus, it is not required, as you will collaborate directly with our dedicated in-house MD scientists.
• Communication: Excellent ability to communicate complex computational and bioinformatic results to experimental colleagues and troubleshoot challenging scientific problems in a fast-paced environment.