PhD in Chemical and Biomolecular Engineering
University of Connecticut College of Engineering Graduate Programs
4 - 5 years
Earliest start date
Graduate work in chemical and biomolecular engineering provides an opportunity for students to further their studies and research. Our graduate program uses cutting edge tools, and resources so that we can contribute to shaping new methodologies for chemical and biomolecular engineering research. We help provide coursework and guided research for the M.S. and Ph.D. students. This exposure allows students to have core principles augmented that students receive as undergraduates. Their research gives unparalleled experience in problem solving, a key component to challenging issues of today for a better tomorrow.
The Ph.D. program emphasizes original and scholarly research in a variety of subject areas within chemical engineering. Throughout their tenure of study, students work on research which culminates in their preparation of a written thesis dissertation and, finally, an oral presentation to the faculty. Many students conduct interdisciplinary research with chemical engineering faculty through the University’s research centers of excellence.
Scholarships and Funding
In order for an applicant to be considered for one of the following fellowships, the applicant must select that they wish to be considered in SLATE. Recipients of these fellowships will be the most academically promising members of the entering class of graduate students at the University of Connecticut. The criteria used to select recipients include the following:
- Evidence of scholarly or creative achievement highlighted by the department or program in their nomination and evidence that the department or program provides the environment necessary for success in the areas of interest highlighted by the applicant.
- Evidence of any prior scholarly or creative achievement by the nominee, e.g., publications, presentations, exhibits, performances.
- Evidence that the nominee has been successful at previous academic institutions, e.g., letters of recommendation.
- Quantitative evidence of academic accomplishment, e.g., undergraduate grade point average, GMAT (when available).
The Jorgensen Fellowship (JF) is available to outstanding young scholars applying to doctoral programs. The award consists of a service-free fellowship providing a $20,000 annual stipend for five years.
In addition, to be eligible for either the fellowships below, applicants must demonstrate a commitment to enhancing diversity in higher education and/or a commitment to enhancing diversity in their field of study.
- The Harriott Fellowship (HF) is available to outstanding young scholars applying to doctoral programs. The award consists of a service-free fellowship providing a $20,000 annual stipend for five years.
- The Crandall Fellowship (CF) is available to outstanding young scholars applying to master’s programs. The award consists of a service-free fellowship providing a $20,000 annual stipend for two years (MFA is for three years).
For HF and CF fellowships students must submit a diversity statement through the SLATE application system. Students can demonstrate a commitment to enhancing diversity in higher education through participation in organizations or activities that (a) directly relate to increasing access to higher education and retention in higher education of individuals, regardless of age, race, sexual orientation, gender, nationality, cultural background, religion, or beliefs or (b) that help to ensure that individuals are welcomed and included in higher education environments regardless of age, race, sexual orientation, gender, nationality, cultural background, religion, or beliefs. Such organization and activities might include participation/affiliation with TRIO programs, cultural/affinity organizations/centers, volunteer experiences, and college or university committees focused on these goals. Students provide evidence of this commitment through research and educational experience reflected on their CV/resume (articles, presentations, internship, and research experience), in their personal statement, or in letters of recommendations.
Three exams are required for the Ph.D. degree. The first is the qualifying exam which is taken within the first two semesters. This exam consists of a written test followed by an oral presentation. The second, known as the general exam, requires the student to prepare a written Ph.D. these proposal and present it orally to a faculty committee. This exam is scheduled on an individual basis after coursework and language requirements have been completed. The final exam is an oral defense of the student’s completed research, following submission of the written thesis
This current MENG concentration is not eligible for UConn visa sponsorship. Please contact ISSS for more information regarding programs that allow UConn visa sponsorship at [email protected].
UConn’s Master of Engineering (MENG) in Chemical Engineering is a 30-credit online (synchronous and asynchronous coursework) graduate degree that helps working engineers strengthen their technical skills helping to bring value to industry. Curriculum focuses on industrial practice and design and integrates subject matter across disciplines helping to prepare graduates for advanced positions in a variety of industries, such as petrochemical processing, materials manufacturing, energy distribution, microelectronics, and biotechnology.
Process Engineering Certificate
The Chemical and Biomolecular Engineering Department offers a fully online with synchronous and asynchronous coursework, 12-credit advanced engineering certificate program in Process Engineering. Process engineering is the merger of fundamental engineering science and knowledge along with empirical information to develop and optimize processes. Process Engineering is primarily grounded in the discipline of Chemical Engineering and its core areas, including thermodynamics, transport phenomenon, and kinetics. The fundamental knowledge for Process Engineering is encoded in mathematical models, whereas the empirical information is represented by data science/machine learning models.