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The Wang lab at the Genomics Research Center (GRC), Academia Sinica in Taipei, is seeking highly motivated and creative researchers to join our team. Our lab focuses on energy metabolism, adipocyte biology, and mitochondrial function, with an emphasis on their roles in metabolic diseases such as obesity, insulin resistance, and non-alcoholic fatty liver disease (NAFLD).
We utilize CRISPR-based functional genomics, small-molecule screening, and mitochondrial biology to uncover novel regulators of brown and beige fat thermogenesis, adipose tissue plasticity, and metabolic homeostasis. Our goal is to develop innovative therapeutic strategies for metabolic disorders.
- Key Research Areas -
* Endothelin 3/EDNRB signaling induces thermogenic differentiation of white adipose tissue. (Nat Commun 2024)
* Brown adipose tissue-derived MaR2 contributes to cold-induced resolution of inflammation. (Nat Metab. 2022)
* CRISPR-engineered human brown-like adipocytes prevent diet-induced obesity and ameliorate metabolic syndrome in mice. (Sci Transl Med. 2020)
* Thermogenic adipocyte biology and metabolic regulation (Nat Rev Endocrinol, 2021)
If you are enthusiastic about metabolism research and aspire to work in an interdisciplinary and collaborative environment, we invite you to apply!

The Wang lab at the Genomics Research Center (GRC), Academia Sinica in Taipei, is seeking highly motivated and creative researchers to join our team. Our lab focuses on energy metabolism, adipocyte biology, and mitochondrial function, with an emphasis on their roles in metabolic diseases such as obesity, insulin resistance, and non-alcoholic fatty liver disease (NAFLD).
We utilize CRISPR-based functional genomics, small-molecule screening, and mitochondrial biology to uncover novel regulators of brown and beige fat thermogenesis, adipose tissue plasticity, and metabolic homeostasis. Our goal is to develop innovative therapeutic strategies for metabolic disorders.
- Key Research Areas -
* Endothelin 3/EDNRB signaling induces thermogenic differentiation of white adipose tissue. (Nat Commun 2024)
* Brown adipose tissue-derived MaR2 contributes to cold-induced resolution of inflammation. (Nat Metab. 2022)
* CRISPR-engineered human brown-like adipocytes prevent diet-induced obesity and ameliorate metabolic syndrome in mice. (Sci Transl Med. 2020)
* Thermogenic adipocyte biology and metabolic regulation (Nat Rev Endocrinol, 2021)
If you are enthusiastic about metabolism research and aspire to work in an interdisciplinary and collaborative environment, we invite you to apply!

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。

本實驗室的研究重點為傳染病及癌症新藥研發，利用結構生物學的技術（X光結晶學，低溫電子顯微鏡）探討新冠肺炎，流感，抗藥性細菌感染以及癌細胞之膜蛋白及醣蛋白結構，作為新藥研發的基礎。
1.藉由分析蛋白質的立體空間結構，作為新藥設計的基礎。
2.運用細胞及動物實驗模型，探討新型藥物之廣度及效能。
My lab foucuses on structural biology and drug discovery in infectious diseases and cancers. X-ray crystallography and cryo-EM are being used to study membrane proteins and glycoproteins in SARS-COV-2, influenza viruses, drug-resistant bacteria and cancers, to provide structural basis for drug-discovery and vaccine design. In vitro cell based assays and in vivo animal models are being used to validate the efficacy of novel vaccines.

【工作內容 / Responsibilities】
本實驗室的研究重點為傳染病及癌症新藥研發，利用結構生物學的技術（X光結晶學，低溫電子顯微鏡）探討新冠肺炎，流感，抗藥性細菌感染以及癌細胞之膜蛋白及醣蛋白結構，作為新藥研發的基礎。
1.藉由分析蛋白質的立體空間結構，作為新藥設計的基礎。
2.運用細胞及動物實驗模型，探討新型藥物之廣度及效能。
My lab foucuses on structural biology and drug discovery in infectious diseases and cancers. X-ray crystallography and cryo-EM are being used to study membrane proteins and glycoproteins in SARS-COV-2, influenza viruses, drug-resistant bacteria and cancers, to provide structural basis for drug-discovery and vaccine design. In vitro cell based assays and in vivo animal models are being used to validate the efficacy of novel vaccines.

Institute of Information Science at Academia Sinica, in collaboration with National Taiwan University, is seeking motivated Research Assistants to work on cutting-edge computer vision and deep learning projects. This position offers a unique opportunity to conduct research under the joint supervision of Prof. Chun-Yi Lee (NTU) and Dr. Chien-Yao Wang (Academia Sinica).

The selected candidate will engage in advanced research projects at the intersection of computer vision and deep learning, with a minimum commitment of eight months. This position is ideal for candidates planning to pursue academic careers or graduate studies abroad, offering valuable research experience and publication opportunities.

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.