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The Hsieh lab at IPMB focuses on fungal-bacterial competition (www.labofpie.org). We
combine functional genomics, genetics, and experimental evolution to determine the
mechanisms and consequences of such cross-kingdom competition.

Our lab will start in October 2025. We are seeking 1-2 M.S. level research associates to help establish our new lab. Candidates will have the opportunity to lead independent projects under Phoebe’s mentorship and earn authorship on resulting publications. This position is ideal for someone with microbiology and molecular biology experience who seeks more hands-on research training.

Responsibilities
• Basic microbiology and molecular biology experiments, including:
o Routine handling of bacterial and fungal culture
o High-throughput genetic screens
o Build gene knockout strains
o Molecular cloning, DNA extractions, and sequencing
• Lab management support, including media prep, ordering, and maintaining equipment
• Candidates are encouraged to lead and execute small-scale independent research projects
• Present data at the lab meeting and provide weekly research updates
• Weekly update on the experimental progress
• 2-year minimum appointment

1.研究助理：協助研究或獨立研究。
Research Assistants: Assist in research-related tasks or work on an independent project.
2.博士後研究員：表觀遺傳與轉錄調控於發育及疾病之角色。
Postdoctoral Fellows: Conduct research on the roles of epigenetics and transcriptional regulation in development and diseases.

針對臺灣地區一般民眾，執行臺灣人體生物資料庫計畫大台北地區宣導及收案作業。
工作內容包括：計畫宣導、問卷訪談、身體檢測、抽血與檢體處理等。

The research interest in our lab is to study the post-transcriptional gene regulation with the approaches of genomics and molecular biology. The projects will focus on RNA splicing, translation and stability. Using human cell lines as our major model system, and combining computational biology, molecular biology and next-generation sequencing, we aim to understand how sequence variations contribute to post-transcriptional regulation in diseases.

Research on Optimization of Deep Learning Model Inference and Training

The Computer Systems Laboratory - Machine Learning Systems team focuses on research areas including parallel and distributed computing, compilers, and computer architecture. We aim to leverage computer system technologies to accelerate the inference and training of deep learning models and develop optimizations for next-generation AI models. Our research emphasizes the following:

1. AI Model Compression and Optimization
Model compression techniques (e.g., pruning and quantization) reduce the size and computational demands of AI models, which are crucial for resource-constrained platforms such as embedded systems and memory-limited AI accelerators. We aim to explore:
* AI compiler: deployment methods for compressed models across servers, edge devices, and heterogeneous systems.
* High performance computing: efficient execution of compressed models on processors with advanced AI extensions, e.g., Intel AVX512, ARM SVE, RISC-V RVV, and tensor-level accelerations on GPUs and NPUs.

2. AI Accelerator Design
We aim to design AI accelerators for accelerating AI model inference, focusing on software and hardware co-design and co-optimization.

3. Optimization of AI Model Inference in Heterogeneous Environments
Computer architectures are evolving toward heterogeneous multi-processor designs (e.g., CPUs + GPUs + AI accelerators). Integrating heterogeneous processors to execute complex models (e.g., hybrid models, multi-models, and multi-task models) with high computational efficiency poses a critical challenge. We aim to explore:
* Efficient scheduling algorithms.
* Parallel algorithms for the three dimensions: data parallelism, model parallelism, and tensor parallelism.

(一) 研究方向：
1. mRNA癌症疫苗的開發研究
2.發展癌症疫苗與細胞免疫治療
(二) 研究內容：
以產品開發為導向，有機會接受初階商業課程訓練，目標為未來臨床應用。

The Institute of Information Science at Academia Sinica, Taiwan, R.O.C. seeks candidates for the position of assistant research fellows(equivalent to the rank of assistant professor), in areas related to Intelligence Computing /Theory/System Researches.
Academia Sinica is a national academic research institution in Taiwan. Led by Dr. James C. Liao, a world-renowned scientist and the chief scientific advisor to the R.O.C. government, Academia Sinica conducts research on a broad spectrum of subjects in science and humanities. The Institute of Information Science is committed to quality research in computer and information science. There are over 300 full-time postdoctoral fellows and research assistants (mostly with master degree) working with a faculty of nearly 40 research fellows on various projects. Excellent computing facilities and lab spaces are available for dedicated long-term research. Current research activities are focused on Algorithms and Computation Theory, Machine Learning, Artificial Intelligence, Quantum Computing, Cryptography, Bioinformatics, Natural Language Processing, Data Mining, Formal Methods, Multimedia, Computer Systems, and Networking.
All candidates should have a Ph.D. degree in computer science or closely related fields with good research background and publication records. Salary is based on individual qualification. Additional compensation up to 40% for the first two to three years is available for applicants with exceptional qualifications. In addition to the budgeted research funding supported within Academia Sinica, external funding from government agencies and industry-sponsored institutions is also available.

Conduct methodological and collaborative research in causal inference, survival analyses and statistical genomics with applications in studies of cancer and chronic diseases.

1. Assist the cooperative with statistical consulting and related services.
2. Responsible for data analysis tasks, including data preprocessing, database construction, data analysis, and report writing.
3. Participate in various activities organized by the Institute of Statistical Science and the cooperative.
4. Assist with administrative affairs of the cooperative.
5. Complete other tasks assigned as needed.

To conduct an innovative research program in computational (broadly-defined), materials, energy or other chemistry areas

To conduct an innovative research program in computational (broadly-defined), materials, energy or other chemistry areas

To perform research on quantum matter theory; more details can be found at https://sites.google.com/view/qmtheory/job-openings.