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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。

1. 執行metagenomics、NGS次世代序列或RNA-sequencing等結果處理及資料分析、統整
2. 執行程式撰寫及相關大數據分析
3. 協助實驗室網頁及相關網路資訊系統管理
4. 協助研究結果彙整與成果報告撰寫
5. 與跨領域學門/實驗室合作，整合及共同分析實驗數據

The lab is using moss (Physcomitrium patens) as a model to
investigate various developmental biology-related questions
in plants. The lab routinely employs techniques such as
genetic manipulations, protein expression/purification, and
confocal microscopy observation. We encourage those
interested in plant biology to be part of the team.
實驗室網站：https://sites.google.com/view/kock-lab/home

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.

Responsibility: (either)
(1) Exploring epigenetic control in shaping sexual dimorphic circuit.
(2) Exploring the neural circuit mechanisms through Drosophila olfactory virtual reality system
(3) Exploring circuit integration of thermo-, hygro-, and olfactory sensation.

Responsibility: (either)
(1) Exploring epigenetic control in shaping sexual dimorphic circuit.
(2) Exploring the neural circuit mechanisms through Drosophila olfactory virtual reality system
(3) Exploring circuit integration of thermo-, hygro-, and olfactory sensation.

誠徵對新興傳染病症研究有熱誠的研究人員

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.

The Center for Survey Research, Research Center
for Humanities and Social Sciences (RCHSS) in
Academia Sinica, Taiwan is looking for motivated
candidates specializing in survey methods,
computational social science, text mining, and data
science to join the Center. We are an
interdisciplinary team of social scientists and data
science researchers with the goal of integrating
different research methods to advance
understanding of human behaviors and social
issues. We invite applications from outstanding
candidates for the tenure-track position at the
Assistant, Associate, or Full Research Fellow level
(equivalent to Assistant, Associate, or Full
Professors, respectively, at university).

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.