Academia Sinica Logo

大氣雲模式及降水粒子運動數值模擬與分析
Perform numerical simulation and analysis of atmospheric cloud model and precipitation particle motions.

1. 主要進行合成生物學研究與代謝體分析，結合基因體學、轉錄體學、蛋白質體學、及AI輔助，研究植物固碳或增加其生產量的調控分子、代謝途徑、或促進生長素。運用分子生物學技術與生化實驗分析，了解合成途徑或特定分子對植物或藍綠菌的生理及生化影響，進一步提出新法以促進光合效率固碳與增加產量。
Studying synthetic biology and metabolomic analyses by combining multi-omics and AI-assisted analysis to explore factors with potential for enhancing plant carbon fixation and productivity. By molecular biology and biochemical experiments, we examine the effects of synthetic pathways and specific molecules on plants and cyanobacteria, contributing to research on improving photosynthetic efficiency and yield.
2. 實驗室日常工作，包含藥品配製，細菌培養，植物種植管理，採購報帳，安全衛生管理。Routine laboratory work, including reagent preparation, bacterial culture, plant cultivation and management, purchasing and reimbursement, and safety and sanitation management.

[Bacterial Evolutionary Genomics] This position involves largescale comparative analyses of genome sequences to investigate genetic diversity and diversification across bacterial lineages. The research focuses on developing species delineation methods to improve taxonomic frameworks and identification accuracy, while also examining key genes associated with bacterial diversification to provide new insights into evolutionary and functional genomics. For related research projects and recent publications, please see our lab website: https://ipmb.sinica.edu.tw/chkuo/

The NanoBioPhotonics Laboratory (NBP Lab) is dedicated to developing advanced optical microscopy technologies and integrating them with chemical and biological techniques. Through interdisciplinary approaches, the lab quantitatively investigates nanoscale structures and dynamics in living cells. Responsibilities include optical system development, operation, and optimization, live-cell experiments, image and data analysis, execution of research projects, and publication of research results.

We conduct research in diverse areas of computational biology, data science and machine learning, including multi-omics data integration of cancers, optimal treatment design for heterogeneous and evolving tumors, single-cell omics data analysis, evolution of biomolecular networks and human populations, topological characterization of networks, and unsupverised learning using deep neural networks. Group members will gain experience in learning domain knowledge, performing scientific reasoning, formulating and solving mathematical models, developing algorithms, writing programs and using existing packages, collecting and analyzing big data, and interacting closely with international and domestic collaborators. We look for motivated persons curious about open scientific problems and willing to dedicate energy and time to solve them.

Dr. Cheng is a neurobiologist. New positions are open for Laboratory Assistants and Postdoctoral Fellows interested in using the mouse to study molecular and cellular mechanisms of axon guidance and adult neurogenesis.

Prospective candidate should have a strong interest in chromatin biology, gene regulation and/or functional genomics. We investigate how disease-associated mutations in chromatin regulators and histones disrupt gene regulation.

Website: https://www.imb.sinica.edu.tw/en/faculty/profile/hskwok.html, https://kwokchromatinlab.org

Our laboratory is currently seeking two postdoctoral researchers and additional research assistants with expertise in molecular and cell biology techniques. Responsibilities include the development of a high-throughput analysis platform to investigate protein degradation and biomolecular interactions. The ultimate goal of this research is the development of targeted protein degradation therapeutics. Interested candidates should submit their CV to hcyen@as.edu.tw.


About us: http://gps.imb.sinica.edu.tw/
About lab: https://www.imb.sinica.edu.tw/en/faculty/profile/hyen.html

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

Overview
We are seeking 1–2 postdoctoral fellows to join our lab in studying polymicrobial
interactions and host–microbe interactions with agricultural relevance. Our research
integrates comparative genomics, ecological sampling, microbial genetics, and
functional assays to understand how fungi, bacteria, and phages interact within
polymicrobial communities and how these interactions influence microbial pathogenesis
in plant hosts.
Our lab has strong expertise in functional genomics and experimental evolution, using
bottom-up approaches to address these questions. We welcome postdoctoral
researchers with complementary interests—such as plant immunity, evolutionary
genomics, or microbial ecology—to join our team. This position is ideal for recently
graduated PhD scientists who are passionate about microbial interactions and aspire to
pursue a faculty position in academia.
The position is open until filled.
Lab website: www.labofpie.org
Research Topics
1. Genetic basis of fungal–bacterial–phage interactions and their impact on
microbial community development
2. Comparative analysis of microbial pathogenic genes in single versus mixed
infections
3. Bioinformatic and evolutionary analysis of antimicrobial genes in plant pathogens
Job Responsibilities
1. Lead and contribute to independent and collaborative research projects
2. Design and conduct experiments and/or computational data analyses
3. Develop and maintain standardized laboratory protocols or bioinformatic
pipelines
4. Actively participate in manuscript preparation and scientific presentations
5. Mentor graduate and undergraduate researchers

Two postdocs needed in two distinct fields:
a) Biogeochemical cycles of greenhouse gases, with emphasis on water, CO2, and/or N2O in the atmosphere
b) Environmental particulate matter pollution, with emphasis on the formation mechanism of nitrates

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.