Biomedical Device
Research Field
Professor Hsieh-Chih Tsai (H-index 40 from Scopus) is the chairman of the Graduate Institute of Applied Science and Technology and the director of the Advanced Membrane Materials Center at National Taiwan University of Science and Technology in Taipei, Taiwan. He has concentrated on the synthesis of functional polymers and the preparation of polymeric nanocomposites as drug carriers, contrast agents, and biomedical devices. We recently developed several kinds of hydrogel platform applied in anti-adhesion, hemostatic and wound dressing. In addition, we have designed a variety of injectable hydrogel systems for local drug delivery in cancer treatment. Data: Hsieh-Chih Tsai(https://orcid.org/0000-0002-7034-6205) has 195 SCI papers in journals such as Advanced Science, ACS Applied Materials and Interfaces, Chemical Engineering Journal, Carbon, and Journal of Materials Chemistry C. In the last three years, he has served as the principal investigator on 11 research projects (5 from NSTC and 6 from biomedical device and pharmaceutical companies).
In biomedical device lab, we are focus on design the functional polymer or preparation the polymer nanocomposite for a new biomedical device for the clinical need. Stundets in our lab will be trained for synthesis and characterization of functional polymers and polymer nanocomposites. And then the obtained new materials will be checked its biocompatibility in vitro. For the potential materials with specific funtion will be conducted with in vivo work.
- Synthesis organic covalent framework nanocomposite as Anionic and Cationic and anionic exchange membrane.
- Synthesis of dynamic crosslinking injectable hydrogel as drug delivery device and biomedical device.
- Preparation of hydrophilic and hydrophobic microparticle as biomedical device
- 2019 Excellent Research Award in National Taiwan University of Science and Technology
- 2020 Outstanding Research Award in National Taiwan University of Science and Technology
- 2020 Excellent Teaching Award in National Taiwan University of Science and Technology
- 2022 Excellent Research Award in National Taiwan University of Science and Technology
2007/07 Ph.D. Department of Chemical Engineering, National Tsing Hua University, Taiwan
2002/07 M.S. Department of Chemical Engineering, Tung Hai University, Taiwan
2000/07 B.S. Department of Chemical Engineering, Tung Hai University, Taiwan
Job Description
Job Description 2 – Biological Evaluation & Publication
Conduct in vitro hemostatic assays, such as:
Whole blood clotting index
Plasma absorption and clot formation time
Platelet adhesion and activation (where applicable)
Evaluate biocompatibility, including cytotoxicity and blood compatibility assays.
Analyze and interpret experimental data with a publication-driven mindset.
Lead or co-lead manuscript preparation for submission to SCI-indexed journals in biomaterials, biomedical engineering, or translational medicine.
Participate in regular research discussions and contribute to grant- or proposal-related technical sections if relevant.
Preferred Intern Educational Level
Current PhD student (materials science, biomedical engineering, polymer science, chemical engineering, or related fields).
Strong motivation to publish high-quality SCI papers during the internship period.
Prior experience with biomaterials, hydrogels, microparticles, or hemostatic materials is highly preferred.
Skill sets or Qualities
Required Skill Sets
Technical Skills
Polymer or biopolymer synthesis and modification
Microparticle fabrication techniques
Standard biomaterials characterization methods
Basic cell culture and in vitro bioassays (or strong willingness to learn)
Scientific data analysis and figure preparation
Research Skills
Experimental design with clear hypotheses
Literature review and critical comparison with existing hemostatic agents
Academic writing in English (manuscripts, figures, supporting information)
Personal Qualities
Strong research ownership and self-motivation
Ability to work independently while integrating into a collaborative research environment
Careful, reproducible experimental practice
Clear scientific communication and openness to feedback
Commitment to completing the project through journal submission
Job Description
Job Description 2 – Fluorescence Validation & AI-Based Cancer Recognition
Set up fluorescence (FL)-based exosome detection systems, such as:
Fluorescent labeling of exosomes
Surface-bound fluorescence readout for capture confirmation
Build dual-validation datasets (electrochemical + fluorescence) for the same exosome samples.
Generate structured datasets (CSV format), including:
Electrical features (impedance spectra, signal shifts)
Optical features (intensity, distribution, temporal response)
Collaborate in or lead AI model development, including:
Feature extraction and normalization
Supervised classification of different cancer exosomes
Performance comparison between single-modality vs dual-modality sensing
Participate in manuscript writing, figures, and supplementary data preparation for SCI journals.
Preferred Intern Educational Level
Preferred Candidate Profile
Current PhD student in:
Biomedical engineering
Materials science
Electrical engineering
Chemical engineering
Applied physics or related fields
Strong interest in liquid biopsy, biosensors, or AI-assisted diagnostics
Clear intention and ability to publish peer-reviewed SCI papers during the internship
Skill sets or Qualities
Required Skill Sets
Experimental & Technical Skills
Surface modification and functional materials for biointerfaces
Basic exosome handling, capture, or characterization (NTA, protein assays, etc.)
Electrochemical measurement techniques (EIS, amperometry, potentiometry)
Fluorescence measurement systems (microscopy, plate reader, or custom optics)
Data processing and experimental reproducibility control
Computational & Data Skills (preferred but not mandatory)
Handling structured datasets (CSV, time-series data)
Basic Python / MATLAB / R for data processing
Familiarity with machine learning concepts (classification, feature selection)
Willingness to collaborate closely with AI-focused researchers
Personal Qualities
Strong system-integration mindset (materials → device → signal → data)
Ability to work independently on experimental setup and troubleshooting
Careful documentation and reproducibility awareness
Clear scientific communication and interdisciplinary openness
High commitment to completing datasets suitable for journal submission